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Greenfield, Bridges Models Pinpoint Inefficiencies in Electric Power Storage

Why It’s Important:

If everyone used electricity at a constant rate, generating power would be simple. Generators that supply a fixed level of energy would be fairly cheap to run. But people use electricity at very different rates during the day. Spikes in use, for example, on a hot afternoon have to be met by firing up seldom-used and expensive generators. It’s even more complicated for commercial and industrial customers: They’re not just charged for the total energy they use, but also if they exceed set levels of energy use. Recent drops in the cost of batteries have made it possible for those customers to smooth out their need for grid power, potentially saving money for everyone. Michael Fisher, working with faculty advisor Jay Apt at Carnegie Mellon University, set out to understand how different scenarios and assumptions about batteries and energy use would affect the economics of “behind the meter” (BTM) batteries—batteries that belong to users or third parties as opposed to power companies—for commercial users.

“We wanted to investigate how BTM battery systems would be used in a commercial application. Residential users just pay energy charges; but commercial users pay for their peak usage as well as how much energy they use ... These peak charges can be up to 50 percent of an industrial or commercial customer’s bill. Batteries can make sense in mitigating those charges.”
—Michael Fisher, Carnegie Mellon University

How PSC and XSEDE Helped:

The CMU researchers used PSC’s interim Greenfield/DXC system and the new XSEDE-allocated Bridges system to model how a fleet of BTM batteries would behave under different assumptions using meter data from 665 commercial and industrial buildings. With help from XSEDE Extended Collaborative Support Service expert Roberto Gomez of PSC, they moved their model, which they’d built on personal computers using the popular statistical software MATLAB, onto the PSC supercomputers. Unusually for a supercomputer, both of these systems run MATLAB directly and so don’t require rewriting the software. The Greenfield run allowed the group to identify the factors most likely to affect the economics of the batteries. In later runs, Bridges’ size allowed the investigators to run computations on many different buildings in parallel, greatly speeding the calculations. The time savings made it possible for the investigators to test many more possible scenarios. The Bridges work showed that most of the wasted energy associated with battery storage (measured via pollutant emissions) stemmed from internal energy losses in the batteries and not the timing of charging and discharging. This points toward what technology improvements may be necessary to make BTM batteries economical in more markets, and the regulatory environment necessary to encourage their development. A paper describing the work is now in the second round of review at an academic journal.

“We forecast what the [electric] load would be for part of the day ... to see what the optimal step would be for the next 15-minute period ... and continued over the whole year. That’s 35,000 steps for each of 665 buildings ... Each step didn’t take very long, but it adds up. It would have taken months to run on a laptop. With Greenfield and Bridges we were able to do it in an hour.”

—Michael Fisher, Carnegie Mellon University

Read the journal article.

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